We report results on the popular CUB, Oxford-102 and MS COCO datasets. The CUB dataset has 200 different categories with 11,788 images of birds in total. The Oxford-102 dataset contains 102 different categories with 8,189 images of flowers in total. As in [1]}, [2]}, we split images into class-disjoint training and test sets. The CUB dataset has 150 train classes and 50 test classes, while the Oxford-102 dataset has 82 train+val and 20 test classes. For both datasets, 10 captions are used per image. The MS COCO dataset totally consists of 123,287 images with 5 sentence annotations. The official training split of COCO is used for training and the official validation split of COCO is used for testing. During mini-batch selection for training, a random image view (e.g. crop, flip) is chosen for one of the captions.
